Data management and analytics in African drylands: Leveraging the power of CGIAR Breeding Analytical Pipeline for data-driven decision making

Breeding is a numbers game, which, being data-driven, needs accurate and precise numbers. The Dryland Crops Program (DCP) of the International Maize and Wheat Improvement Center (CIMMYT), in collaboration with National Agricultural Research and Extension Systems (NARES) partners, the International Institute of Tropical Agriculture (IITA), the Alliance of Biodiversity International & CIAT, and other African institutions, has established the African Dryland Crop Improvement Network (ADCIN).

Under the capacity-building initiative of the ADCIN, a comprehensive training on modern biometrics, quantitative genetics, and data management practices was organized from June 10-14, 2024, in Nairobi, Kenya. This training was part of several capacity building events approved by the ADCIN Eastern and Southern Africa (ESA) Steering Committee through a competitive process.

The training was proposed by East and Southern African NARES Breeding Leads Dr. Rachael Kisilu of KALRO (Kenya), Dr. George Karwani of TARI (Tanzania), and Dr. Dagnachew Besha of EIAR-DZARC (Ethiopia), in collaboration with Dr. Abhishek Rathore, Breeding Data and Informatics Expert at CIMMYT.  

The training brought together 43 participants from nine East and Southern African (ESA) countries, representing a wide array of roles and crops, including chickpea, finger millet, pearl millet, pigeonpea, and sorghum. The group demonstrated gender balance, with 42% women and 58% men who participated. 

This diverse group, comprising breeding leaders, data champions and young breeders, engaged in interactive sessions designed to enhance their skills in modern biometrics, quantitative genetics, and data management practices. The training highlighted the critical importance of data-driven decision-making in breeding programs and emphasized the need for standardized protocols and advanced tools to manage complex research datasets effectively. 

The week-long training was structured to provide both theoretical knowledge and practical application through hands-on exercises. Sessions covered a wide range of topics, from the basics of the experimental design to advanced data analysis techniques using the CGIAR Breeding Analytical Pipeline. Participants were introduced to the Breeding Management System (BMS), digital data collection methods, quality control processes, and strategies for managing multilocation trials and genotype x environment (GxE) interactions.

A significant highlight of the training was the practical use of the CGIAR Breeding Analytical Pipeline. This tool was demonstrated to streamline breeding data analysis, enabling participants to efficiently process large datasets and derive meaningful insights for crop improvement.  

Throughout the workshop, CGIAR Breeding Analytical Pipeline was used as the main backbone for data analytics. Participants were trained in data importing, quality checks, outlier detection, single and multi-trial analysis, and stability analysis using the Finlay-Wilkinson model.  

The inclusion of gender-diverse participants, particularly women plant breeders, researchers, and agricultural officers, underscored the workshop’s commitment to fostering inclusive and equitable capacity building in NARES institutions. Participants learned to leverage the pipeline for tasks like trial analysis, appreciating its potential to accelerate breeding cycles and improve decision-making processes.

The integration of this pipeline into daily workflows is expected to enhance the accuracy and efficiency of breeding efforts across the CGIAR – NARES network, ultimately contributing to the development of more robust and productive crop varieties. 

Related links

• News story: Soft launch of CGIAR Breeding Analytics Pipeline enables complex quantitative genetic analyses for CGIAR crop breeders
• News story: Strengthening global plant breeding: the innovation journey of CGIAR Breeding Analytics Pipeline 

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We would like to thank all funders who support this research through their contributions to the CGIAR Trust Fund.